text: #!/usr/bin/env python
  # MIT License
  #
  # Copyright (c) 2018 Károly Harsányi
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  import cv2
  import torch
  import argparse
  from network import DensenetUnetHybrid
  import image_utils
  
  def predict_img(img_path):
      """Inference a single image."""
      # switch to CUDA device
      device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
      print('Use GPU: {}'.format(str(device) != 'cpu'))
  
      # load model
      print('Loading model...')
      model = DensenetUnetHybrid.load_pretrained(device=device)
      model.eval()
  
      # load image
      img = cv2.imread(img_path)[..., ::-1]
      img = image_utils.scale_image(img)
      img = image_utils.center_crop(img)
      inp = image_utils.img_transform(img)
      inp = inp[None, :, :, :].to(device)
  
      # inference
      print('Running the image through the network...')
      output = model(inp)
  
      # transform and plot the results
      output = output.cpu()[0].data.numpy()
      image_utils.show_img_and_pred(img, output)
  
  
  def get_arguments():
      """Get command line arguments."""
      parser = argparse.ArgumentParser()
      parser.add_argument('-i', '--img_path', required=True, type=str, help='Path to the input image.')
      return parser.parse_args()
  
  
  if __name__ == '__main__':
      args = get_arguments()
      predict_img(args.img_path)
  